Hispanic-Latinx Heritage Month ACS Data
suzanne
8/19/2021
Notes for Hispanic-Latinx heritage month
These are notes for Hispanic heritage month. https://www.hispanicheritagemonth.gov/ September 15 to October 15
library(devtools)## Warning: package 'devtools' was built under R version 4.0.5## Loading required package: usethis## Warning: package 'usethis' was built under R version 4.0.5library(sf)## Warning: package 'sf' was built under R version 4.0.5## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library(dplyr)## Warning: package 'dplyr' was built under R version 4.0.5##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(psrccensus)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5Sys.getenv("CENSUS_API_KEY")## [1] "c4780eb03010d73b7ae4e6894c1592375e545a21"I searched the api table list for Hispanic to see what data is available. https://api.census.gov/data/2019/acs/acs5/variables.html
How does Hispanic age breakdown compare to non-Hispanic? * Age is found in B1002I, B1002A
What Races do Hispanic people identify with? * B3002
What specific origins do people come from? * B300, B05003
What means of transportation do people use to get to work? * B06004, B06007 means of transportation to work for workplace geo
Information about grandparents living with grandchildren * B08105H; I grandparents B10051I
Are more Hispanic women giving birth than all women? * B13002 women who gave birth
What are the educational attainment levels for Hispanic people? * B15002 educational attainment
What are the poverty rates for Hispanic people? * B17001 B19001
Median Age by Hispanic and All
overall_age_df <- get_acs_recs(geography = 'county',
table.names = c('B01002'),
years=c(2019),
acs.type = 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2019 1-year ACShispanic_age_df<-get_acs_recs(geography = 'county',
table.names = c('B01002I'),
years=c(2019),
acs.type = 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2019 1-year ACSoverall_hispanic_df<-rbind(overall_age_df, hispanic_age_df)
age_df<-overall_hispanic_df%>% filter(label=='Estimate!!Median age --!!Total:' & name !='Region') %>% mutate('Hispanic'=ifelse(variable=='B01002_001', 'All Population', 'Hispanic or Latino Population'))
write.table(age_df,"clipboard", sep='\t', row.names=FALSE )
age_df## # A tibble: 8 x 12
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King County Washington B01002_~ 36.9 0.2 Esti~ MEDIAN~ County
## 2 53035 Kitsap County Washington B01002_~ 39.5 0.3 Esti~ MEDIAN~ County
## 3 53053 Pierce County Washington B01002_~ 36.4 0.2 Esti~ MEDIAN~ County
## 4 53061 Snohomish County Washington B01002_~ 38.2 0.3 Esti~ MEDIAN~ County
## 5 53033 King County Washington B01002I~ 28.5 0.4 Esti~ MEDIAN~ County
## 6 53035 Kitsap County Washington B01002I~ 27.3 0.3 Esti~ MEDIAN~ County
## 7 53053 Pierce County Washington B01002I~ 25.2 0.2 Esti~ MEDIAN~ County
## 8 53061 Snohomish County Washington B01002I~ 26.4 0.3 Esti~ MEDIAN~ County
## # ... with 3 more variables: acs_type <chr>, year <dbl>, Hispanic <chr>ggplot(data=age_df, aes(x=name, y=estimate, fill=Hispanic))+geom_bar(stat='identity', position=position_dodge())+
geom_text(aes(label=estimate), vjust=1.6, color="white",
position = position_dodge(0.9), size=3.5)
Hispanic Population by Tract
tract.big.tbl <- psrccensus::get_decennial_recs(geography='tract',table_codes=c('P005'),year=c(2010))## Getting data from the 2010 decennial Census## Loading SF1 variables for 2010 from table P005. To cache this dataset for faster access to Census tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per Census dataset.## Using Census Summary File 1## Getting data from the 2010 decennial Census## Loading SF1 variables for 2010 from table P005. To cache this dataset for faster access to Census tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per Census dataset.## Using Census Summary File 1## Getting data from the 2010 decennial Census## Loading SF1 variables for 2010 from table P005. To cache this dataset for faster access to Census tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per Census dataset.## Using Census Summary File 1## Getting data from the 2010 decennial Census## Loading SF1 variables for 2010 from table P005. To cache this dataset for faster access to Census tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per Census dataset.## Using Census Summary File 1tract.tbl <- tract.big.tbl %>%
filter(label=='Total!!Hispanic or Latino')
gdb.nm <- paste0("MSSQL:server=",
"AWS-PROD-SQL\\Sockeye",
";database=",
"ElmerGeo",
";trusted_connection=yes")
spn <- 2285
tract_layer_name <- "dbo.tract2010_nowater"
tract.lyr <- st_read(gdb.nm, tract_layer_name, crs = spn)## Reading layer `dbo.tract2010_nowater' from data source
## `MSSQL:server=AWS-PROD-SQL\Sockeye;database=ElmerGeo;trusted_connection=yes'
## using driver `MSSQLSpatial'
## Simple feature collection with 773 features and 19 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 1099353 ymin: -97548.53 xmax: 1622631 ymax: 477101.5
## Projected CRS: NAD83 / Washington North (ftUS)m<-create_tract_map(tract.tbl=tract.tbl, tract.lyr=tract.lyr,
legend.title='Hispanic Population', legend.subtitle='by Census Tract')
mWomen who gave birth
women_birth_df<-get_acs_recs(geography = 'county',
table.names = c('B13002'),
years=c(2019),
acs.type = 'acs1') ## The 1-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2019 1-year ACSwomen_birth_df_hispanic<-get_acs_recs(geography = 'county',
table.names = c('B13002I'),
years=c(2019),
acs.type = 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2019 1-year ACSwomen_birth_df## # A tibble: 95 x 11
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King County Washington B13002_001 576759 2190 Esti~ WOMEN ~ County
## 2 53033 King County Washington B13002_002 27413 2965 Esti~ WOMEN ~ County
## 3 53033 King County Washington B13002_003 23243 2931 Esti~ WOMEN ~ County
## 4 53033 King County Washington B13002_004 42 69 Esti~ WOMEN ~ County
## 5 53033 King County Washington B13002_005 14458 2394 Esti~ WOMEN ~ County
## 6 53033 King County Washington B13002_006 8743 1484 Esti~ WOMEN ~ County
## 7 53033 King County Washington B13002_007 4170 1499 Esti~ WOMEN ~ County
## 8 53033 King County Washington B13002_008 382 528 Esti~ WOMEN ~ County
## 9 53033 King County Washington B13002_009 2960 1053 Esti~ WOMEN ~ County
## 10 53033 King County Washington B13002_010 828 631 Esti~ WOMEN ~ County
## # ... with 85 more rows, and 2 more variables: acs_type <chr>, year <dbl>women_birth_df_hispanic## # A tibble: 35 x 11
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King County Washington B13002I_001 62809 637 Esti~ WOMEN ~ County
## 2 53033 King County Washington B13002I_002 2679 949 Esti~ WOMEN ~ County
## 3 53033 King County Washington B13002I_003 2272 918 Esti~ WOMEN ~ County
## 4 53033 King County Washington B13002I_004 407 341 Esti~ WOMEN ~ County
## 5 53033 King County Washington B13002I_005 60130 1196 Esti~ WOMEN ~ County
## 6 53033 King County Washington B13002I_006 25881 2306 Esti~ WOMEN ~ County
## 7 53033 King County Washington B13002I_007 34249 2352 Esti~ WOMEN ~ County
## 8 53035 Kitsap County Washington B13002I_001 5837 406 Esti~ WOMEN ~ County
## 9 53035 Kitsap County Washington B13002I_002 345 295 Esti~ WOMEN ~ County
## 10 53035 Kitsap County Washington B13002I_003 228 251 Esti~ WOMEN ~ County
## # ... with 25 more rows, and 2 more variables: acs_type <chr>, year <dbl>1049422 women age 15 to 50 in the region
55604 gave birth (5.3%)
118645 Hispanic women
6694 (5.6%)
Poverty Rates
poverty_df_white <- get_acs_recs(geography = 'county',
table.names = c('B17020H'),
years=c(2019),
acs.type = 'acs5')## Getting data from the 2015-2019 5-year ACSpoverty_df_hispanic<- get_acs_recs(geography = 'county',
table.names = c('B17020I'),
years=c(2019),
acs.type = 'acs5')## Getting data from the 2015-2019 5-year ACSpoverty_df_white## # A tibble: 85 x 11
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King County Washington B17020H_001 1292995 1267 Esti~ POVERT~ County
## 2 53033 King County Washington B17020H_002 81042 2514 Esti~ POVERT~ County
## 3 53033 King County Washington B17020H_003 3260 489 Esti~ POVERT~ County
## 4 53033 King County Washington B17020H_004 2898 339 Esti~ POVERT~ County
## 5 53033 King County Washington B17020H_005 3507 486 Esti~ POVERT~ County
## 6 53033 King County Washington B17020H_006 51922 1872 Esti~ POVERT~ County
## 7 53033 King County Washington B17020H_007 12354 811 Esti~ POVERT~ County
## 8 53033 King County Washington B17020H_008 4234 554 Esti~ POVERT~ County
## 9 53033 King County Washington B17020H_009 2867 444 Esti~ POVERT~ County
## 10 53033 King County Washington B17020H_010 1211953 2871 Esti~ POVERT~ County
## # ... with 75 more rows, and 2 more variables: acs_type <chr>, year <dbl>poverty_df_hispanic## # A tibble: 85 x 11
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King County Washington B17020I_001 209087 461 Esti~ POVERT~ County
## 2 53033 King County Washington B17020I_002 30076 2556 Esti~ POVERT~ County
## 3 53033 King County Washington B17020I_003 4527 673 Esti~ POVERT~ County
## 4 53033 King County Washington B17020I_004 4567 712 Esti~ POVERT~ County
## 5 53033 King County Washington B17020I_005 3488 581 Esti~ POVERT~ County
## 6 53033 King County Washington B17020I_006 16134 1320 Esti~ POVERT~ County
## 7 53033 King County Washington B17020I_007 1007 296 Esti~ POVERT~ County
## 8 53033 King County Washington B17020I_008 291 122 Esti~ POVERT~ County
## 9 53033 King County Washington B17020I_009 62 61 Esti~ POVERT~ County
## 10 53033 King County Washington B17020I_010 179011 2629 Esti~ POVERT~ County
## # ... with 75 more rows, and 2 more variables: acs_type <chr>, year <dbl>Median Income
income_df_white <- get_acs_recs(geography = 'county',
table.names = c('B19013H'),
years=c(2019),
acs.type = 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2019 1-year ACSincome_df_hispanic<- get_acs_recs(geography = 'county',
table.names = c('B19013I'),
years=c(2019),
acs.type = 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2019 1-year ACSwhite_hispanic_df<-merge(income_df_white, income_df_hispanic, by ='name')
white_hispanic_df## name GEOID.x state.x variable.x estimate.x moe.x
## 1 King County 53033 Washington B19013H_001 109124 2900.000
## 2 Kitsap County 53035 Washington B19013H_001 81317 4099.000
## 3 Pierce County 53053 Washington B19013H_001 83992 2468.000
## 4 Region REGION Washington B19013H_001 363379 6431.024
## 5 Snohomish County 53061 Washington B19013H_001 88946 3171.000
## label.x
## 1 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 2 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 3 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 4 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 5 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## concept.x
## 1 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (WHITE ALONE, NOT HISPANIC OR LATINO HOUSEHOLDER)
## 2 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (WHITE ALONE, NOT HISPANIC OR LATINO HOUSEHOLDER)
## 3 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (WHITE ALONE, NOT HISPANIC OR LATINO HOUSEHOLDER)
## 4 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (WHITE ALONE, NOT HISPANIC OR LATINO HOUSEHOLDER)
## 5 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (WHITE ALONE, NOT HISPANIC OR LATINO HOUSEHOLDER)
## census_geography.x acs_type.x year.x GEOID.y state.y variable.y
## 1 County acs1 2019 53033 Washington B19013I_001
## 2 County acs1 2019 53035 Washington B19013I_001
## 3 County acs1 2019 53053 Washington B19013I_001
## 4 County acs1 2019 REGION Washington B19013I_001
## 5 County acs1 2019 53061 Washington B19013I_001
## estimate.y moe.y
## 1 78175 7656.00
## 2 66667 26481.00
## 3 67212 7294.00
## 4 286104 29580.61
## 5 74050 7871.00
## label.y
## 1 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 2 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 3 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 4 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## 5 Estimate!!Median household income in the past 12 months (in 2019 inflation-adjusted dollars)
## concept.y
## 1 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (HISPANIC OR LATINO HOUSEHOLDER)
## 2 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (HISPANIC OR LATINO HOUSEHOLDER)
## 3 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (HISPANIC OR LATINO HOUSEHOLDER)
## 4 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (HISPANIC OR LATINO HOUSEHOLDER)
## 5 MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (HISPANIC OR LATINO HOUSEHOLDER)
## census_geography.y acs_type.y year.y
## 1 County acs1 2019
## 2 County acs1 2019
## 3 County acs1 2019
## 4 County acs1 2019
## 5 County acs1 2019write.table(white_hispanic_df,"clipboard", sep='\t', row.names=FALSE )Means of Transportation to Work
white_transport_df <- get_acs_recs(geography = 'county',
table.names = c('B08105A'),
years=c(2019),
acs.type = 'acs5')## Getting data from the 2015-2019 5-year ACShispanic_transport_df <- get_acs_recs(geography = 'county',
table.names = c('B08105I'),
years=c(2019),
acs.type = 'acs5')## Getting data from the 2015-2019 5-year ACSwhite_hispanic_df<-rbind(white_transport_df, hispanic_transport_df)%>%filter(name=='Region')
write.table(white_hispanic_df,"clipboard", sep='\t', row.names=FALSE )
white_hispanic_df## # A tibble: 14 x 11
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 REGION Region Washington B08105A_001 1508149 5040. Esti~ MEANS ~ County
## 2 REGION Region Washington B08105A_002 1047108 6154. Esti~ MEANS ~ County
## 3 REGION Region Washington B08105A_003 125851 2867. Esti~ MEANS ~ County
## 4 REGION Region Washington B08105A_004 132724 2411. Esti~ MEANS ~ County
## 5 REGION Region Washington B08105A_005 58884 1938. Esti~ MEANS ~ County
## 6 REGION Region Washington B08105A_006 37356 1374. Esti~ MEANS ~ County
## 7 REGION Region Washington B08105A_007 106226 2501. Esti~ MEANS ~ County
## 8 REGION Region Washington B08105I_001 194800 1818. Esti~ MEANS ~ County
## 9 REGION Region Washington B08105I_002 128135 1991. Esti~ MEANS ~ County
## 10 REGION Region Washington B08105I_003 29012 1499. Esti~ MEANS ~ County
## 11 REGION Region Washington B08105I_004 19386 1217. Esti~ MEANS ~ County
## 12 REGION Region Washington B08105I_005 7749 700. Esti~ MEANS ~ County
## 13 REGION Region Washington B08105I_006 3371 504. Esti~ MEANS ~ County
## 14 REGION Region Washington B08105I_007 7147 665. Esti~ MEANS ~ County
## # ... with 2 more variables: acs_type <chr>, year <dbl>Country of Origin
country_df_hispanic<- get_acs_recs(geography = 'county',
table.names = c('B03001'),
years=c(2019),
acs.type = 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2019 1-year ACScountry_df_hispanic## # A tibble: 155 x 11
## GEOID name state variable estimate moe label concept census_geography
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King County Washington B03001_001 2252782 NA Esti~ HISPAN~ County
## 2 53033 King County Washington B03001_002 2030140 NA Esti~ HISPAN~ County
## 3 53033 King County Washington B03001_003 222642 NA Esti~ HISPAN~ County
## 4 53033 King County Washington B03001_004 159516 7614 Esti~ HISPAN~ County
## 5 53033 King County Washington B03001_005 9798 2693 Esti~ HISPAN~ County
## 6 53033 King County Washington B03001_006 4339 1426 Esti~ HISPAN~ County
## 7 53033 King County Washington B03001_007 1521 996 Esti~ HISPAN~ County
## 8 53033 King County Washington B03001_008 21608 5056 Esti~ HISPAN~ County
## 9 53033 King County Washington B03001_009 947 570 Esti~ HISPAN~ County
## 10 53033 King County Washington B03001_010 4903 1718 Esti~ HISPAN~ County
## # ... with 145 more rows, and 2 more variables: acs_type <chr>, year <dbl>